Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations2007
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory345.1 KiB
Average record size in memory176.1 B

Variable types

Numeric15
Categorical7

Alerts

four_g is highly overall correlated with three_gHigh correlation
front_camera is highly overall correlated with primary_cameraHigh correlation
price_range is highly overall correlated with ramHigh correlation
primary_camera is highly overall correlated with front_cameraHigh correlation
ram is highly overall correlated with price_rangeHigh correlation
three_g is highly overall correlated with four_gHigh correlation
phone_id is uniformly distributed Uniform
price_range is uniformly distributed Uniform
phone_id has unique values Unique
sc_w has 180 (9.0%) zeros Zeros
front_camera has 476 (23.7%) zeros Zeros
primary_camera has 101 (5.0%) zeros Zeros

Reproduction

Analysis started2024-12-19 15:42:16.380212
Analysis finished2024-12-19 15:42:30.317081
Duration13.94 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

phone_id
Real number (ℝ)

Uniform  Unique 

Distinct2007
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1004
Minimum1
Maximum2007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:30.364759image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.3
Q1502.5
median1004
Q31505.5
95-th percentile1906.7
Maximum2007
Range2006
Interquartile range (IQR)1003

Descriptive statistics

Standard deviation579.51531
Coefficient of variation (CV)0.57720649
Kurtosis-1.2
Mean1004
Median Absolute Deviation (MAD)502
Skewness0
Sum2015028
Variance335838
MonotonicityStrictly increasing
2024-12-19T10:42:30.445768image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2007 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
1991 1
 
< 0.1%
1990 1
 
< 0.1%
1989 1
 
< 0.1%
Other values (1997) 1997
99.5%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2007 1
< 0.1%
2006 1
< 0.1%
2005 1
< 0.1%
2004 1
< 0.1%
2003 1
< 0.1%
2002 1
< 0.1%
2001 1
< 0.1%
2000 1
< 0.1%
1999 1
< 0.1%
1998 1
< 0.1%

battery_power
Real number (ℝ)

Distinct1094
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.4549
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:30.526587image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile570.3
Q1851
median1225
Q31616.5
95-th percentile1929.7
Maximum1998
Range1497
Interquartile range (IQR)765.5

Descriptive statistics

Standard deviation439.84999
Coefficient of variation (CV)0.35516028
Kurtosis-1.2270807
Mean1238.4549
Median Absolute Deviation (MAD)382
Skewness0.032191247
Sum2485579
Variance193468.01
MonotonicityNot monotonic
2024-12-19T10:42:30.599306image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1589 6
 
0.3%
1872 6
 
0.3%
618 6
 
0.3%
1310 5
 
0.2%
1063 5
 
0.2%
832 5
 
0.2%
1083 5
 
0.2%
1821 5
 
0.2%
504 5
 
0.2%
1413 5
 
0.2%
Other values (1084) 1954
97.4%
ValueCountFrequency (%)
501 2
 
0.1%
502 2
 
0.1%
503 3
0.1%
504 5
0.2%
506 1
 
< 0.1%
507 2
 
0.1%
508 3
0.1%
509 1
 
< 0.1%
510 3
0.1%
511 4
0.2%
ValueCountFrequency (%)
1998 1
 
< 0.1%
1997 1
 
< 0.1%
1996 2
0.1%
1995 2
0.1%
1994 3
0.1%
1993 1
 
< 0.1%
1992 2
0.1%
1991 4
0.2%
1989 2
0.1%
1988 1
 
< 0.1%

clock_speed
Real number (ℝ)

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5214748
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:30.668317image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.81601139
Coefficient of variation (CV)0.53632921
Kurtosis-1.323694
Mean1.5214748
Median Absolute Deviation (MAD)0.8
Skewness0.17848569
Sum3053.6
Variance0.66587459
MonotonicityNot monotonic
2024-12-19T10:42:30.730565image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5 416
20.7%
2.8 85
 
4.2%
2.3 78
 
3.9%
2.1 76
 
3.8%
1.6 76
 
3.8%
2.5 75
 
3.7%
0.6 74
 
3.7%
1.4 70
 
3.5%
1.3 68
 
3.4%
1.5 67
 
3.3%
Other values (16) 922
45.9%
ValueCountFrequency (%)
0.5 416
20.7%
0.6 74
 
3.7%
0.7 64
 
3.2%
0.8 58
 
2.9%
0.9 58
 
2.9%
1 61
 
3.0%
1.1 51
 
2.5%
1.2 57
 
2.8%
1.3 68
 
3.4%
1.4 70
 
3.5%
ValueCountFrequency (%)
3 28
 
1.4%
2.9 62
3.1%
2.8 85
4.2%
2.7 55
2.7%
2.6 55
2.7%
2.5 75
3.7%
2.4 58
2.9%
2.3 78
3.9%
2.2 60
3.0%
2.1 76
3.8%

m_dep
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50254111
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:30.785907image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.28828665
Coefficient of variation (CV)0.57365785
Kurtosis-1.2750624
Mean0.50254111
Median Absolute Deviation (MAD)0.3
Skewness0.083433924
Sum1008.6
Variance0.083109193
MonotonicityNot monotonic
2024-12-19T10:42:30.838632image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1 320
15.9%
0.2 213
10.6%
0.8 210
10.5%
0.5 205
10.2%
0.7 202
10.1%
0.3 199
9.9%
0.9 196
9.8%
0.6 188
9.4%
0.4 168
8.4%
1 106
 
5.3%
ValueCountFrequency (%)
0.1 320
15.9%
0.2 213
10.6%
0.3 199
9.9%
0.4 168
8.4%
0.5 205
10.2%
0.6 188
9.4%
0.7 202
10.1%
0.8 210
10.5%
0.9 196
9.8%
1 106
 
5.3%
ValueCountFrequency (%)
1 106
 
5.3%
0.9 196
9.8%
0.8 210
10.5%
0.7 202
10.1%
0.6 188
9.4%
0.5 205
10.2%
0.4 168
8.4%
0.3 199
9.9%
0.2 213
10.6%
0.1 320
15.9%

mobile_wt
Real number (ℝ)

Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.28002
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:30.902320image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.358784
Coefficient of variation (CV)0.25205859
Kurtosis-1.2066153
Mean140.28002
Median Absolute Deviation (MAD)31
Skewness0.005295089
Sum281542
Variance1250.2436
MonotonicityNot monotonic
2024-12-19T10:42:30.980716image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 28
 
1.4%
185 27
 
1.3%
101 27
 
1.3%
199 26
 
1.3%
146 26
 
1.3%
88 25
 
1.2%
198 25
 
1.2%
105 25
 
1.2%
131 24
 
1.2%
145 24
 
1.2%
Other values (111) 1750
87.2%
ValueCountFrequency (%)
80 21
1.0%
81 13
0.6%
82 15
0.7%
83 19
0.9%
84 17
0.8%
85 13
0.6%
86 19
0.9%
87 15
0.7%
88 25
1.2%
89 24
1.2%
ValueCountFrequency (%)
200 19
0.9%
199 26
1.3%
198 25
1.2%
197 19
0.9%
196 20
1.0%
195 11
0.5%
194 16
0.8%
193 15
0.7%
192 15
0.7%
191 15
0.7%

n_cores
Real number (ℝ)

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5181863
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:31.042957image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2884127
Coefficient of variation (CV)0.50648922
Kurtosis-1.2305799
Mean4.5181863
Median Absolute Deviation (MAD)2
Skewness0.0052450612
Sum9068
Variance5.2368326
MonotonicityNot monotonic
2024-12-19T10:42:31.096022image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 274
13.7%
7 259
12.9%
8 257
12.8%
2 249
12.4%
3 247
12.3%
5 247
12.3%
1 243
12.1%
6 231
11.5%
ValueCountFrequency (%)
1 243
12.1%
2 249
12.4%
3 247
12.3%
4 274
13.7%
5 247
12.3%
6 231
11.5%
7 259
12.9%
8 257
12.8%
ValueCountFrequency (%)
8 257
12.8%
7 259
12.9%
6 231
11.5%
5 247
12.3%
4 274
13.7%
3 247
12.3%
2 249
12.4%
1 243
12.1%

ram
Real number (ℝ)

High correlation 

Distinct1562
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2124.9008
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:31.160562image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile445.3
Q11208.5
median2148
Q33063.5
95-th percentile3825.7
Maximum3998
Range3742
Interquartile range (IQR)1855

Descriptive statistics

Standard deviation1083.7371
Coefficient of variation (CV)0.51001771
Kurtosis-1.1904271
Mean2124.9008
Median Absolute Deviation (MAD)930
Skewness0.0048566465
Sum4264676
Variance1174486
MonotonicityNot monotonic
2024-12-19T10:42:31.233749image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1229 4
 
0.2%
2610 4
 
0.2%
2227 4
 
0.2%
1464 4
 
0.2%
3142 4
 
0.2%
1037 3
 
0.1%
2819 3
 
0.1%
2190 3
 
0.1%
2775 3
 
0.1%
1277 3
 
0.1%
Other values (1552) 1972
98.3%
ValueCountFrequency (%)
256 1
< 0.1%
258 2
0.1%
259 1
< 0.1%
262 1
< 0.1%
263 1
< 0.1%
265 1
< 0.1%
267 1
< 0.1%
273 1
< 0.1%
277 1
< 0.1%
278 2
0.1%
ValueCountFrequency (%)
3998 1
< 0.1%
3996 1
< 0.1%
3993 1
< 0.1%
3991 2
0.1%
3990 1
< 0.1%
3984 1
< 0.1%
3978 1
< 0.1%
3971 1
< 0.1%
3970 2
0.1%
3969 1
< 0.1%

talk_time
Real number (ℝ)

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.016941
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:31.303064image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4612486
Coefficient of variation (CV)0.49571371
Kurtosis-1.2174124
Mean11.016941
Median Absolute Deviation (MAD)5
Skewness0.0088026214
Sum22111
Variance29.825236
MonotonicityNot monotonic
2024-12-19T10:42:31.362260image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7 125
 
6.2%
4 123
 
6.1%
15 116
 
5.8%
16 116
 
5.8%
19 114
 
5.7%
6 111
 
5.5%
10 106
 
5.3%
11 104
 
5.2%
8 104
 
5.2%
20 102
 
5.1%
Other values (9) 886
44.1%
ValueCountFrequency (%)
2 99
4.9%
3 94
4.7%
4 123
6.1%
5 93
4.6%
6 111
5.5%
7 125
6.2%
8 104
5.2%
9 101
5.0%
10 106
5.3%
11 104
5.2%
ValueCountFrequency (%)
20 102
5.1%
19 114
5.7%
18 101
5.0%
17 98
4.9%
16 116
5.8%
15 116
5.8%
14 101
5.0%
13 100
5.0%
12 99
4.9%
11 104
5.2%

price_range
Categorical

High correlation  Uniform 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
503 
2
503 
3
501 
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2007
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 503
25.1%
2 503
25.1%
3 501
25.0%
0 500
24.9%

Length

2024-12-19T10:42:31.428731image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T10:42:31.486480image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 503
25.1%
2 503
25.1%
3 501
25.0%
0 500
24.9%

Most occurring characters

ValueCountFrequency (%)
1 503
25.1%
2 503
25.1%
3 501
25.0%
0 500
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 503
25.1%
2 503
25.1%
3 501
25.0%
0 500
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 503
25.1%
2 503
25.1%
3 501
25.0%
0 500
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 503
25.1%
2 503
25.1%
3 501
25.0%
0 500
24.9%

px_height
Real number (ℝ)

Distinct1137
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.84604
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:31.556249image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.3
Q1283.5
median567
Q3949
95-th percentile1484.1
Maximum1960
Range1960
Interquartile range (IQR)665.5

Descriptive statistics

Standard deviation443.95025
Coefficient of variation (CV)0.68739331
Kurtosis-0.32692926
Mean645.84604
Median Absolute Deviation (MAD)320
Skewness0.66048099
Sum1296213
Variance197091.82
MonotonicityNot monotonic
2024-12-19T10:42:31.633982image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
347 7
 
0.3%
179 6
 
0.3%
275 6
 
0.3%
371 6
 
0.3%
211 5
 
0.2%
667 5
 
0.2%
293 5
 
0.2%
730 5
 
0.2%
674 5
 
0.2%
327 5
 
0.2%
Other values (1127) 1952
97.3%
ValueCountFrequency (%)
0 2
0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
0.1%
4 3
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 2
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1960 1
< 0.1%
1949 1
< 0.1%
1920 1
< 0.1%
1914 1
< 0.1%
1901 1
< 0.1%
1899 1
< 0.1%
1895 1
< 0.1%
1878 1
< 0.1%
1874 1
< 0.1%
1869 1
< 0.1%

px_width
Real number (ℝ)

Distinct1109
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1252.1978
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:31.708871image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile580
Q1875.5
median1247
Q31633.5
95-th percentile1929.7
Maximum1998
Range1498
Interquartile range (IQR)758

Descriptive statistics

Standard deviation432.31416
Coefficient of variation (CV)0.34524431
Kurtosis-1.1870507
Mean1252.1978
Median Absolute Deviation (MAD)377
Skewness0.013585838
Sum2513161
Variance186895.53
MonotonicityNot monotonic
2024-12-19T10:42:31.902672image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1247 7
 
0.3%
874 7
 
0.3%
1988 6
 
0.3%
1383 6
 
0.3%
1463 6
 
0.3%
1469 6
 
0.3%
1552 5
 
0.2%
1079 5
 
0.2%
1234 5
 
0.2%
1781 5
 
0.2%
Other values (1099) 1949
97.1%
ValueCountFrequency (%)
500 2
0.1%
501 2
0.1%
503 1
 
< 0.1%
506 1
 
< 0.1%
507 4
0.2%
508 1
 
< 0.1%
509 2
0.1%
510 3
0.1%
511 2
0.1%
512 2
0.1%
ValueCountFrequency (%)
1998 1
 
< 0.1%
1997 1
 
< 0.1%
1996 1
 
< 0.1%
1995 3
0.1%
1994 2
 
0.1%
1992 1
 
< 0.1%
1991 1
 
< 0.1%
1990 1
 
< 0.1%
1989 3
0.1%
1988 6
0.3%

sc_h
Real number (ℝ)

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.308919
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:31.965165image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2111795
Coefficient of variation (CV)0.34212424
Kurtosis-1.1907672
Mean12.308919
Median Absolute Deviation (MAD)4
Skewness-0.099478249
Sum24704
Variance17.734033
MonotonicityNot monotonic
2024-12-19T10:42:32.020095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17 195
 
9.7%
12 157
 
7.8%
7 151
 
7.5%
16 144
 
7.2%
14 143
 
7.1%
15 135
 
6.7%
13 132
 
6.6%
11 127
 
6.3%
10 125
 
6.2%
9 125
 
6.2%
Other values (5) 573
28.6%
ValueCountFrequency (%)
5 97
4.8%
6 114
5.7%
7 151
7.5%
8 118
5.9%
9 125
6.2%
10 125
6.2%
11 127
6.3%
12 157
7.8%
13 132
6.6%
14 143
7.1%
ValueCountFrequency (%)
19 124
6.2%
18 120
6.0%
17 195
9.7%
16 144
7.2%
15 135
6.7%
14 143
7.1%
13 132
6.6%
12 157
7.8%
11 127
6.3%
10 125
6.2%

sc_w
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7623318
Minimum0
Maximum18
Zeros180
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:32.077832image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3528123
Coefficient of variation (CV)0.75539078
Kurtosis-0.38590632
Mean5.7623318
Median Absolute Deviation (MAD)3
Skewness0.63507132
Sum11565
Variance18.946975
MonotonicityNot monotonic
2024-12-19T10:42:32.139073image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 211
10.5%
3 200
10.0%
4 182
9.1%
0 180
9.0%
5 161
 
8.0%
2 158
 
7.9%
7 133
 
6.6%
6 130
 
6.5%
8 127
 
6.3%
10 107
 
5.3%
Other values (9) 418
20.8%
ValueCountFrequency (%)
0 180
9.0%
1 211
10.5%
2 158
7.9%
3 200
10.0%
4 182
9.1%
5 161
8.0%
6 130
6.5%
7 133
6.6%
8 127
6.3%
9 97
4.8%
ValueCountFrequency (%)
18 8
 
0.4%
17 19
 
0.9%
16 29
 
1.4%
15 31
 
1.5%
14 33
 
1.6%
13 49
2.4%
12 68
3.4%
11 84
4.2%
10 107
5.3%
9 97
4.8%

front_camera
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3059292
Minimum0
Maximum19
Zeros476
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:32.196979image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.341375
Coefficient of variation (CV)1.0082319
Kurtosis0.27838955
Mean4.3059292
Median Absolute Deviation (MAD)3
Skewness1.0214839
Sum8642
Variance18.847537
MonotonicityNot monotonic
2024-12-19T10:42:32.255172image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 476
23.7%
1 246
12.3%
2 190
 
9.5%
3 171
 
8.5%
5 139
 
6.9%
4 134
 
6.7%
6 112
 
5.6%
7 100
 
5.0%
9 78
 
3.9%
8 77
 
3.8%
Other values (10) 284
14.2%
ValueCountFrequency (%)
0 476
23.7%
1 246
12.3%
2 190
 
9.5%
3 171
 
8.5%
4 134
 
6.7%
5 139
 
6.9%
6 112
 
5.6%
7 100
 
5.0%
8 77
 
3.8%
9 78
 
3.9%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 11
 
0.5%
17 6
 
0.3%
16 24
 
1.2%
15 23
 
1.1%
14 20
 
1.0%
13 41
2.0%
12 45
2.2%
11 51
2.5%
10 62
3.1%

primary_camera
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9088191
Minimum0
Maximum20
Zeros101
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:32.316976image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0586332
Coefficient of variation (CV)0.61143847
Kurtosis-1.1691836
Mean9.9088191
Median Absolute Deviation (MAD)5
Skewness0.019813921
Sum19887
Variance36.707036
MonotonicityNot monotonic
2024-12-19T10:42:32.381692image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 123
 
6.1%
7 120
 
6.0%
9 113
 
5.6%
20 110
 
5.5%
14 105
 
5.2%
1 104
 
5.2%
0 101
 
5.0%
2 100
 
5.0%
17 99
 
4.9%
6 97
 
4.8%
Other values (11) 935
46.6%
ValueCountFrequency (%)
0 101
5.0%
1 104
5.2%
2 100
5.0%
3 93
4.6%
4 95
4.7%
5 59
2.9%
6 97
4.8%
7 120
6.0%
8 89
4.4%
9 113
5.6%
ValueCountFrequency (%)
20 110
5.5%
19 83
4.1%
18 82
4.1%
17 99
4.9%
16 88
4.4%
15 92
4.6%
14 105
5.2%
13 85
4.2%
12 90
4.5%
11 79
3.9%

blue
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1013 
1
994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2007
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1013
50.5%
1 994
49.5%

Length

2024-12-19T10:42:32.448506image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T10:42:32.499188image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1013
50.5%
1 994
49.5%

Most occurring characters

ValueCountFrequency (%)
0 1013
50.5%
1 994
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1013
50.5%
1 994
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1013
50.5%
1 994
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1013
50.5%
1 994
49.5%

dual_sim
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1022 
0
985 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2007
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1022
50.9%
0 985
49.1%

Length

2024-12-19T10:42:32.560934image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T10:42:32.617301image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1022
50.9%
0 985
49.1%

Most occurring characters

ValueCountFrequency (%)
1 1022
50.9%
0 985
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1022
50.9%
0 985
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1022
50.9%
0 985
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1022
50.9%
0 985
49.1%

four_g
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1047 
0
960 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2007
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1047
52.2%
0 960
47.8%

Length

2024-12-19T10:42:32.676398image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T10:42:32.732968image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1047
52.2%
0 960
47.8%

Most occurring characters

ValueCountFrequency (%)
1 1047
52.2%
0 960
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1047
52.2%
0 960
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1047
52.2%
0 960
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1047
52.2%
0 960
47.8%

three_g
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1529 
0
478 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2007
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1529
76.2%
0 478
 
23.8%

Length

2024-12-19T10:42:32.790195image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T10:42:32.842731image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1529
76.2%
0 478
 
23.8%

Most occurring characters

ValueCountFrequency (%)
1 1529
76.2%
0 478
 
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1529
76.2%
0 478
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1529
76.2%
0 478
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1529
76.2%
0 478
 
23.8%

touch_screen
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1009 
0
998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2007
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1009
50.3%
0 998
49.7%

Length

2024-12-19T10:42:32.901561image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T10:42:32.953951image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1009
50.3%
0 998
49.7%

Most occurring characters

ValueCountFrequency (%)
1 1009
50.3%
0 998
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1009
50.3%
0 998
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1009
50.3%
0 998
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1009
50.3%
0 998
49.7%

wifi
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1016 
0
991 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2007
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1016
50.6%
0 991
49.4%

Length

2024-12-19T10:42:33.007763image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T10:42:33.059836image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1016
50.6%
0 991
49.4%

Most occurring characters

ValueCountFrequency (%)
1 1016
50.6%
0 991
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1016
50.6%
0 991
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1016
50.6%
0 991
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1016
50.6%
0 991
49.4%

int_memory
Real number (ℝ)

Distinct63
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.027902
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-12-19T10:42:33.121824image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.146496
Coefficient of variation (CV)0.56658398
Kurtosis-1.2173377
Mean32.027902
Median Absolute Deviation (MAD)16
Skewness0.058541683
Sum64280
Variance329.29533
MonotonicityNot monotonic
2024-12-19T10:42:33.193420image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 47
 
2.3%
14 45
 
2.2%
16 45
 
2.2%
2 42
 
2.1%
57 42
 
2.1%
7 41
 
2.0%
44 40
 
2.0%
42 40
 
2.0%
30 39
 
1.9%
10 38
 
1.9%
Other values (53) 1588
79.1%
ValueCountFrequency (%)
2 42
2.1%
3 25
1.2%
4 20
1.0%
5 36
1.8%
6 37
1.8%
7 41
2.0%
8 37
1.8%
9 35
1.7%
10 38
1.9%
11 34
1.7%
ValueCountFrequency (%)
64 31
1.5%
63 30
1.5%
62 21
1.0%
61 27
1.3%
60 27
1.3%
59 18
0.9%
58 36
1.8%
57 42
2.1%
56 27
1.3%
55 29
1.4%

Interactions

2024-12-19T10:42:29.142605image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:16.901737image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:17.768440image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:18.719952image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:19.545555image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:20.355971image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:21.290346image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:22.118059image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:22.964922image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:23.824028image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:24.791734image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:25.670988image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:26.490352image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:27.425509image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:28.279372image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:29.199318image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:16.965240image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:17.827078image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:18.775562image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:19.599386image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:20.414828image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:21.347340image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:22.176087image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:23.025618image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:23.881900image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:24.853503image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:25.729501image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:26.548640image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:27.484994image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:28.336608image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:29.254863image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:22.565466image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:27.877816image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:28.730634image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:17.426601image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:28.794491image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:17.482636image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:18.436647image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:19.270831image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:20.079814image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:20.910674image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:21.838581image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:22.681899image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:23.541950image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:25.380388image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:26.218040image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:28.854466image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:23.597899image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:24.559632image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:26.270934image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:28.911646image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:29.911055image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:17.594672image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:18.547279image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:19.379850image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:21.946918image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:23.653498image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:24.620096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:25.492538image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:26.325793image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:27.260816image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:28.111636image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-19T10:42:28.223798image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-19T10:42:29.086283image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-19T10:42:33.253102image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
battery_powerblueclock_speeddual_simfour_gfront_cameraint_memorym_depmobile_wtn_coresphone_idprice_rangeprimary_camerapx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifi
battery_power1.0000.0380.0080.0530.0000.038-0.0040.0320.002-0.030-0.0250.1290.0320.009-0.010-0.003-0.029-0.0270.0540.0000.0000.000
blue0.0381.0000.0470.0260.0000.0000.0600.0270.0000.0000.0080.0000.0000.0000.0000.0000.0000.0390.0000.0160.0000.007
clock_speed0.0080.0471.0000.0200.048-0.0050.003-0.0160.011-0.006-0.0080.000-0.005-0.015-0.0110.005-0.031-0.012-0.0110.0410.0350.000
dual_sim0.0530.0260.0201.0000.0000.0000.0000.0620.0350.0000.0000.0000.0000.0000.0000.0190.0000.0200.0200.0000.0000.000
four_g0.0000.0000.0480.0001.0000.0410.0000.0000.0510.0410.0430.0090.0000.0120.0000.0000.0800.0000.0380.5820.0000.000
front_camera0.0380.000-0.0050.0000.0411.000-0.0260.0120.027-0.016-0.0000.0000.659-0.021-0.0110.018-0.011-0.002-0.0010.0000.0440.048
int_memory-0.0040.0600.0030.0000.000-0.0261.0000.006-0.035-0.029-0.0600.040-0.0330.000-0.0070.0320.0400.014-0.0040.0300.0230.008
m_dep0.0320.027-0.0160.0620.0000.0120.0061.0000.023-0.005-0.0160.0180.0270.0280.025-0.009-0.023-0.0200.0170.0000.0680.015
mobile_wt0.0020.0000.0110.0350.0510.027-0.0350.0231.000-0.021-0.0620.0300.0180.0100.000-0.003-0.034-0.0190.0070.0000.0000.000
n_cores-0.0300.000-0.0060.0000.041-0.016-0.029-0.005-0.0211.0000.0120.000-0.001-0.0050.0240.0070.0010.0310.0130.0190.0000.000
phone_id-0.0250.008-0.0080.0000.043-0.000-0.060-0.016-0.0620.0121.0000.000-0.008-0.025-0.004-0.046-0.023-0.005-0.0200.0160.0000.000
price_range0.1290.0000.0000.0000.0090.0000.0400.0180.0300.0000.0001.0000.0270.0840.1030.7230.0350.0590.0000.0000.0190.000
primary_camera0.0320.000-0.0050.0000.0000.659-0.0330.0270.018-0.001-0.0080.0271.000-0.0150.0030.0280.005-0.0350.0140.0000.0350.000
px_height0.0090.000-0.0150.0000.012-0.0210.0000.0280.010-0.005-0.0250.084-0.0151.0000.469-0.0310.0550.027-0.0120.0230.0000.061
px_width-0.0100.000-0.0110.0000.000-0.011-0.0070.0250.0000.024-0.0040.1030.0030.4691.0000.0040.0250.0230.0040.0000.0000.035
ram-0.0030.0000.0050.0190.0000.0180.032-0.009-0.0030.007-0.0460.7230.028-0.0310.0041.0000.0170.0270.0120.0410.0000.000
sc_h-0.0290.000-0.0310.0000.080-0.0110.040-0.023-0.0340.001-0.0230.0350.0050.0550.0250.0171.0000.469-0.0190.0190.0180.072
sc_w-0.0270.039-0.0120.0200.000-0.0020.014-0.020-0.0190.031-0.0050.059-0.0350.0270.0230.0270.4691.000-0.0200.0490.0000.000
talk_time0.0540.000-0.0110.0200.038-0.001-0.0040.0170.0070.013-0.0200.0000.014-0.0120.0040.012-0.019-0.0201.0000.0360.0450.000
three_g0.0000.0160.0410.0000.5820.0000.0300.0000.0000.0190.0160.0000.0000.0230.0000.0410.0190.0490.0361.0000.0000.000
touch_screen0.0000.0000.0350.0000.0000.0440.0230.0680.0000.0000.0000.0190.0350.0000.0000.0000.0180.0000.0450.0001.0000.000
wifi0.0000.0070.0000.0000.0000.0480.0080.0150.0000.0000.0000.0000.0000.0610.0350.0000.0720.0000.0000.0000.0001.000

Missing values

2024-12-19T10:42:30.111485image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-19T10:42:30.257279image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

phone_idbattery_powerclock_speedm_depmobile_wtn_coresramtalk_timeprice_rangepx_heightpx_widthsc_hsc_wfront_cameraprimary_camerabluedual_simfour_gthree_gtouch_screenwifiint_memory
018422.20.6188225491912075697120000017
1210210.50.7136326317290519881730611111053
235630.50.91455260392126317161122611111041
346152.50.813162769112121617861680910010010
4518211.20.6141214111511208121282131410111044
5618590.50.716411067101100416541713701010022
6718211.70.813983220183381101813841000110110
788422.20.6188225491912075697120000017
8910210.50.7136326317290519881730611111053
9105630.50.91455260392126317161122611111041
phone_idbattery_powerclock_speedm_depmobile_wtn_coresramtalk_timeprice_rangepx_heightpx_widthsc_hsc_wfront_cameraprimary_camerabluedual_simfour_gthree_gtouch_screenwifiint_memory
1997199816172.40.8851296707431426538910110036
1998199918822.00.8113835792034743198111900111044
199920006742.90.219831180405761809631411011121
2000200114670.50.61225396253888109915110010011118
200120028582.20.1841397833528141617161200011050
200220037940.50.81066668190122218901340141111102
2003200419652.60.218742032162915196511100311011139
2004200519110.90.710883057538681632911301111036
2005200615120.90.1145586919033667018104500111146
200620075102.00.9168639192348375419451611111145